
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


SCREAMING_SNAKE_CASE :		Optional[int]       			=							logging.get_logger(__name__)


if is_torch_available():
						import torch
						from torch import nn
						from torch.utils.data import Dataset

						from transformers import Trainer






						class        __lowercase		(       _A							):


											def __init__(						self							,							a__ = 1_0_1    )		->				Tuple:





															'''simple docstring'''
															A_													=	length


											def __len__(						self    )		->				int:





															'''simple docstring'''
															return self.length


											def __getitem__(						self							,							a__    )		->				List[str]:





															'''simple docstring'''
															return i






						class        __lowercase		:


											def __call__(						self							,							a__    )		->				Optional[int]:





															'''simple docstring'''
															return {"input_ids": torch.tensor(a_    ), "labels": torch.tensor(a_    )}






						class        __lowercase		(       nn.Module							):


											def __init__(						self    )		->				Optional[Any]:





															'''simple docstring'''
															super().__init__()
															# Add some (unused) params otherwise DDP will complain.
															A_													=	nn.Linear(1_2_0							,							8_0    )


											def 							lowerCAmelCase_     (						self							,							a__							,							a__=None    )		->				str:





															'''simple docstring'''
															if labels is not None:
																			return torch.tensor(0.0							,							device=input_ids.device    ), input_ids
															else:
																			return input_ids






class        __lowercase		(       _A							):


					@require_torch_neuroncore
					def 							lowerCAmelCase_     (						self    )		->				Dict:





									'''simple docstring'''
									A_													=	F"--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        ".split()
									A_													=	self.get_auto_remove_tmp_dir()
									A_													=	F"--output_dir {output_dir}".split()
									A_													=	["torchrun"] + distributed_args + args
									execute_subprocess_async(a_							,							env=self.get_env()    )
									# successful return here == success - any errors would have caused an error in the sub-call






class        __lowercase		(       _A							):


					@require_torch_multi_gpu
					def 							lowerCAmelCase_     (						self    )		->				Optional[Any]:





									'''simple docstring'''
									A_													=	F"--nproc_per_node={torch.cuda.device_count()}\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        ".split()
									A_													=	self.get_auto_remove_tmp_dir()
									A_													=	F"--output_dir {output_dir}".split()
									A_													=	["torchrun"] + distributed_args + args
									execute_subprocess_async(a_							,							env=self.get_env()    )
									# successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
						# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
						#
						# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

						SCREAMING_SNAKE_CASE :		Tuple       			=							HfArgumentParser((TrainingArguments,))
						SCREAMING_SNAKE_CASE :		Tuple       			=							parser.parse_args_into_dataclasses()[0]

						logger.warning(
						    f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, '''
						    f'''distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}'''
						)

						# Essentially, what we want to verify in the distributed case is that we get all samples back,
						# in the right order. (this is crucial for prediction for instance)
						for dataset_length in [101, 40, 7]:
												SCREAMING_SNAKE_CASE :		List[str]       			=							DummyDataset(dataset_length)



												def 							lowerCamelCase_	( __UpperCamelCase  ):
																A_													=	list(range(len(a_  )  )  )
																A_													=	p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
																if not success and training_args.local_rank == 0:
																				logger.warning(
																				    '''Predictions and/or labels do not match expected results:\n  - predictions: '''
																				    F"{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}"  )
																return {"success": success}

												SCREAMING_SNAKE_CASE :		Dict       			=							Trainer(
												    model=DummyModel(),
												    args=training_args,
												    data_collator=DummyDataCollator(),
												    eval_dataset=dataset,
												    compute_metrics=compute_metrics,
												)
												SCREAMING_SNAKE_CASE :		int       			=							trainer.evaluate()
												logger.info(metrics)
												if metrics["eval_success"] is not True:
																		logger.error(metrics)
																		exit(1)

												SCREAMING_SNAKE_CASE :		List[Any]       			=							trainer.predict(dataset)
												logger.info(p.metrics)
												if p.metrics["test_success"] is not True:
																		logger.error(p.metrics)
																		exit(1)

												SCREAMING_SNAKE_CASE :		Union[str, Any]       			=							2

												SCREAMING_SNAKE_CASE :		str       			=							trainer.evaluate()
												logger.info(metrics)
												if metrics["eval_success"] is not True:
																		logger.error(metrics)
																		exit(1)

												SCREAMING_SNAKE_CASE :		Optional[Any]       			=							trainer.predict(dataset)
												logger.info(p.metrics)
												if p.metrics["test_success"] is not True:
																		logger.error(p.metrics)
																		exit(1)



												SCREAMING_SNAKE_CASE :		Optional[int]       			=							None






'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCAmelCase									=   {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}


if TYPE_CHECKING:
					from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
					import sys

					lowerCAmelCase									=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



